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Effective decision making to allocate public funds for energy technology research, development, and demonstration (R&D) requires considering alternative investment opportunities that can have large but highly uncertain returns and a multitude of positive or negative interactions. This paper proposes and implements a method to support R&D decisions that propagates uncertainty through an economic model to estimate the benefits of an R&D portfolio, accounting for innovation spillovers and technology substitution and complementarity. The proposed method improves on the existing literature by: (a) using estimates of the impact of R&D investments from one of the most comprehensive sets of expert elicitations on this topic to date; (b) using a detailed energy-economic model to estimate evaluation metrics relevant to an energy R&D portfolio: e.g., system benefits, technology diffusion, and uncertainty around outcomes; and (c) using a novel sampling and optimization strategy to calculate optimal R&D portfolios. This design is used to estimate an optimal energy R&D portfolio that maximizes the net economic benefits under an R&D budget constraint. Results parameterized based on expert elicitations conducted in 2009-2011 in the United States provide indicative results that show: (1) an expert-recommended portfolio in 2030, relative to the BAU portfolio, can reduce carbon dioxide emissions by 46 million tonnes, increase economic surplus by $29 billion, and increase renewable energy generation by 39 TWh; (2) uncertainty around the estimates of R&D benefits is large and overall uncertainty increases with greater investment levels; (3) a 10-fold expansion from 2012 levels in the annual R&D budget for utility-scale energy storage, bioenergy, advanced vehicles, fossil energy, nuclear energy, and solar photovoltaic technologies can be justified by returns to economic surplus; (4) the greatest returns to public R&D investment are in energy storage and solar photovoltaics; and (5) the current allocation of energy R&D funds is very different from optimal portfolios. Taken together, these results demonstrate the utility of applying new methods to improve the cost-effectiveness and environmental performance in a deliberative approach to energy R&D portfolio decision making.

This paper investigates the extent to which clients were able to influence performance measurement appraisals during the downturn in commercial property markets that began in the UK during the second half of 2007. The sharp change in market sentiment produced speculation that different client categories were attempting to influence their appraisers in different ways. In particular, it was recognised that the requirement for open-ended funds to meet redemptions gave them strong incentives to ensure that their asset values were marked down to market. Using data supplied by Investment Property Databank, we demonstrate that, indeed, unlisted open ended funds experienced sharper drops in capital values than other fund types in the second half of 2007, after the market turning point. These differences are statistically significant and cannot simply be explained by differences in portfolio composition. Client influence on appraisal forms one possible explanation of the results observed: the different pressures on fund managers resulting in different appraisal outcomes.

Abstract Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to starting energy system modelling in developing countries, thereby causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for Algeria, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and two stylized scenarios (Fossil Future and Least Cost) for 2020–2050. The assumptions used and results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work.

SummaryThe relationship between alcohol dependence and psychopathology in patients with alcoholic liver disease was studied, using an alcohol‐dependence screening questionnaire and a structured psychiatric interview schedule. Patients with evidence of psychopathology scored significantly higher on the alcohol dependence questionnaire than those without (median scores 14 versus 9; p<.05). The degree of alcohol dependence was more severe in males than females (median scores 16 versus 8; p<.005). Patients with more than one type of psychopathology were more likely to become dependent on alcohol than those with only a single disorder (median scores 35 versus 9; p<.01.As a therapeutic goal, abstinence alone may often be enough to lead to substantial improvement in liver function in these patients, but this goal cannot be easily attained unless the underlying psychopathology is also identified and treated.

Abstract Moderate or Intense Low-oxygen Dilution (MILD) combustion has drawn increasing attention as it allows to avoid the thermo-chemical conditions prone to the formation of pollutant species while ensuring high energy efficiency and fuel flexibility. MILD combustion is characterized by a strong competition between turbulent mixing and chemical kinetics so that turbulence-chemistry interactions are naturally strengthened and unsteady phenomena such as local extinction and re-ignition may occur. The underlying physical mechanisms are not fully understood yet and the validation of combustion models featuring enhanced predictive capabilities is required. Within this context, high-fidelity data from Direct Numerical Simulation (DNS) represent a great opportunity for the assessment and the validation of combustion closure formulations. In this study, the performance of the Partially Stirred Reactor (PaSR) combustion model in MILD conditions is a priori assessed on Direct Numerical Simulations (DNS) of turbulent combustion of MILD mixtures in a cubical domain. Modeled quantities of interest, such as heat release rate and reaction rates of major and minor species, are compared to the corresponding filtered quantities extracted from the DNS. Different submodels for the key model parameters, i.e., the chemical time scale τc and the mixing time scale τmix, are considered and their influence on the results is evaluated. The results show that the mixing time scale is the leading scale in the investigated cases. The best agreement with the DNS data regarding the prediction of heat release rate and chemical source terms is achieved by the PaSR model that employs a local dynamic approach for the estimation of the mixing time scale. An overestimation of the OH species source terms occurs in limited zones of the computational domain, characterized by low heat release rates.

AbstractEfforts to enable fast charging and high energy density lithium‐ion batteries (LIBs) are hampered by the trade‐off nature of the traditional electrode design: increasing the areal capacity usually comes with sacrificing the fast charge transfer. Here a single‐layer chunky particle electrode design is reported, where red‐phosphorus active material is embedded in nanochannels of vertically aligned graphene (red‐P/VAG) assemblies. Such an electrode design addresses the sluggish charge transfer stemming from the high tortuosity and inner particle/electrode resistance of traditional electrode architectures consisting of randomly stacked active particles. The vertical ion‐transport nanochannels and electron‐transfer conductive nanowalls of graphene confine the direction of charge transfer to minimize the transfer distance, and the incomplete filling of nanochannels in the red‐P/VAG composite buffers volume change locally, thus avoiding the variation of electrodes thickness during cycling. The single‐layer chunky particle electrode displays a high areal capacity (5.6 mAh cm−2), which is the highest among the reported fast‐charging battery chemistries. Paired with a high‐loading LiNi0.6Co0.2Mn0.2O2 (NCM622) cathode, a pouch cell shows stable cycling with high energy and power densities. Such a single‐layer chunky particle electrode design can be extended to other advanced battery systems and boost the development of LIBs with fast‐charging capability and high energy density.

1. Human infectious diseases are a significant threat to global human health and economies (e.g., Ebola, SARs), with the majority of infectious diseases having an animal source (zoonotic). Despite their importance, the lack of a quantitative predictive framework hampers our understanding of how spill-overs of zoonotic infectious diseases into the human population will be impacted by global environmental stressors. 2. Here, we create an environmental-mechanistic model for understanding the impact of global change on the probability of zoonotic disease reservoir host-human spill-over events. As a case study, we focus on Lassa fever virus (LAS). We firstly quantify the spatial determinants of LAS outbreaks, including the phylogeographic distribution of its reservoir host Natal multimammate rat (Mastomys natalensis) (LAS host). Secondly, we use these determinants to inform our environmental-mechanistic model to estimate present day LAS spill-over events and the predicted impact of climate change, human population growth, and land use by 2070. 3. We find phylogeographic evidence to suggest that LAS is confined to only one clade of LAS host (Western clade Mastomys natalensis), and that the probability of its occurrence was a major determinant of the spatial variation in LAS historical outbreaks (69.8%), along with human population density (20.4%). Our estimates for present day LAS spill-over events from our environmental-mechanistic model were consistent with observed patterns, and we predict an increase in events per year by 2070 from 195,125 to 406,725 within the LAS endemic western African region. Of the component drivers, climate change and human population growth are predicted to have the largest effects by increasing landscape suitability for the host and human-host contact rates, while land use change has only a weak impact on the number of future events. 4. LAS spill-over events did not respond uniformly to global environmental stressors, and we suggest that understanding the impact of global change on ...